Object recognition, such as face recognition, includes identifying a person from a database of images captured by an image sensor such as a camera and typically includes learning a facial image. The representation of the captured image is compared with the representation of facial images in the database using a metric to return a closest match. Face recognition includes pre-processing steps such as face detection and face alignment.
Object recognition within a visual image captured by a camera, may be utilized in a variety of industries or applications, including defense, transportation or law enforcement, among others. For example, it may be desirable to identify one or more objects such as a car, a pedestrian, and a building, within an image. Conventional object detection approaches may not provide a desired reliability in accurately identifying target objects and/or may provide a greater than desired number of false positive identifications (e.g., detecting a non-target object as a target object.)
Pedestrian detection in an image currently plays an essential role in various aspects of video surveillance, person identification, and advanced driver assistance systems (ADAS). Real-time, accurate detection of pedestrians is important for practical adoption of such systems. A pedestrian detection method aims to draw bounding boxes that precisely describe the locations of all pedestrians in an image, in real-time processing speed.